Analysis Techniques Research Articles

Quality and customer loyalty in the port services of Manzanillo

The customs industry, particularly within the Mexican context, plays a significant role both domestically and internationally, owing to its contribution to the global economy. With the expansion and standardization of customs clearance services, there has emerged a necessity for providers of these services to seek customer loyalty. Grounded in the principles of quality, the study examines the elements of the SERVQUAL model: tangibles, reliability, responsiveness, assurance, and empathy. Goals:The aim of the study was to extend the SERVQUAL model by incorporating customer attitude as an additional component within the three primary dimensions for measuring service quality. Methodology: A survey was employed to gather data from 100 customs brokerage firms through simple random sampling. Given the utilization of structural equation modeling (SEM) methodology to assess the theoretical model, Partial Least Squares (PLS) analysis technique was applied using Smart PLS 3.3.0 software. The findings indicated that the extended model had a significant impact on both customer satisfaction and loyalty within the customs facilities of Manzanillo, Colima. The coefficients of determination were 61.5% for customer satisfaction and 54% for customer loyalty, with a root mean square error of approximation (SRMR) of 0.065, confirming the model's predictive validity. Contributions: Customs facilities should focus their strategies on addressing issues that may be impacting service quality, particularly concerning sensitivity, empathy, and reliability factors, as these have a moderate level of influence on quality determination, according to customs agencies' perceptions. Regarding the inherent limitations of online surveys and their application in specialized groups, it is crucial to expand the research to include other clients of the customs facility.

Hazard Identification and Safety Education on The Clothing Convection Industry Business in Semarang

The clothing convection industry is one of the most popular businesses, with many growing businesses in Indonesia. In this industry, there are potential hazards that may occur either from work environment factors, the use of tools or machines, or from workers. Research objective to be achieved is to identify potential hazards in the clothing convection industry X and Y. This research used descriptive research, and the sample was two workers at the clothing convection industry X and four workers at the clothing convection industry Y for the data. The sample selection used random sampling. The data analysis technique used a narrative analysis. To minimize the number of work accidents or prevent accidents in the workplace, the identification of potential hazards is carried out through hazard analysis methods; one method is HAZOPS (Hazard Operability Studies). Based on the results of identification from workers and observation using HAZOPS method to minimize potential danger that can occur in each production with safety education using video to convey information easily for workers. Education on the use of PPE is also provided so that workers have safety knowledge and awareness at work. X and Y convection have various potential dangers in the clothing convection industry. Therefore, immediate action and attention from management are needed to minimize work accidents.

PENERAPAN PERILAKU PROSOSIAL TERHADAP ANAK USIA 5-6 TAHUN DI PAUD DOA IBU

This research aims to find out and understand the "Implementation of Prosocial Behavior for Children Aged 5-6 Years in the Doa Ibu PAUD". The type of approach used in this research is a descriptive qualitative approach. Data collection techniques in this research are observation, interviews and documentation. The sources of informants in this research were the class teachers and the school principal. Checking the validity of the data was carried out by triangulating techniques and sources. The data analysis techniques for this research are data reduction, data presentation, and drawing conclusions. Based on the results of research at PAUD Doa Ibu, teachers have implemented this prosocial behavior with various strategies given to children.

Teachers’ Use of Code Switching to Pashto Language in Classroom Discussion: Experiences of non-Pashto Speaking Students in University of Peshawar

The purpose of the study was to explore the learning experiences of non-Pashto speaking students, which ensued due to teachers’ code switching to Pashto language at University of Peshawar. Employing qualitative methods, the objectives of the study included: exploring perceptions of students about teachers’ use of code switching on non-Pashto speaking students’ learning; analyzing the difficulties students’ face; and exploring strategies that students use to overcome the barriers of language in learning. Population of the study consisted of students of University of Peshawar. Using snowball sampling technique, 25 participants were selected from three faculties of University of Peshawar. The data were collected through semi-structured in-depth interviews and analyzed using thematic analysis technique. Findings of the study reveal that some teachers code switch to Pashto, the local language which creates barriers in learning for the non-Pashto speaking students. This code switching affects students’ academically, behaviorally and emotionally. Consequently, students face an obstructive classroom climate, which severely influences their level of attention and interest and results in their non-participative role in class. The study recommends considering inclusive education principles in linguistically diverse classrooms, so that all the students are treated and benefited equally. Moreover, it suggests that teachers adhere to the National Professional Standards for Teachers in Pakistan (NPSTs) to enhance learning effectiveness.

Skin Cancer Detection Based on Machine Learning

Skin cancer, particularly melanoma, poses a significant health risk, accounting for the majority of skin cancer-related fatalities in the United States. Despite representing a small fraction of skin cancer cases, melanoma has one of the highest death rates, stressing the critical need for early detection. The American Cancer Society projects approximately 100,640 new melanoma cases and approximately 8,290 related deaths for 2024, yet also notes high survival rates when caught early. In this context, our study employs machine learning to enhance early detection, analyzing a large dataset from The International Skin Imaging Collaboration (ISIC) featuring over 53,000 images across various skin conditions. We assess Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Decision Tree classifiers through both binary and multiclass frameworks, with Principal Component Analysis (PCA) aiding in data dimensionality reduction and visualization. Our findings reveal SVM and K-NN as effective for binary classification, with K-NN excelling in multiclass scenarios. These results underscore the promise of machine learning in clinical settings, offering a path toward improved skin cancer diagnostic tools and underlining the importance of algorithm refinement and sophisticated data analysis techniques.

Legal liability of mediators

The aim of the research is to identify possible gaps in the regulation of the legal liability of mediators and parties to the mediation process. The research methodology involved the use of dialectical, formal-legal, and comparative legal methods, as well as techniques of analysis and synthesis, deduction and induction, philosophical laws of unity and struggle of opposites, denial of negation, and others. In the main content, the legal liability of mediators is analyzed from the perspective of its positive and negative components, and the legal character of various manifestations of the legal liability of mediators and their place in the modern system of legal liability is identified. Significant attention is paid to the positive liability of mediators and its legal character is justified. In conclusion it is clarified that the institution of mediation as a whole and the legal liability of mediators in Russia are in the process of formation. It is proven that the legal liability of mediators has a public-law rather than a private-law character. Legal gaps in the regulation of the legal liability of mediators, as well as other parties to the mediation process, are identified, and ways to eliminate them are proposed.

Impact of graphene nanosheets on adhesion and corrosion performance of reinforced polyurethane coating for aerospace aluminium alloy 2024-T3

This investigation aims to create an integrated multilayer coating system for the surface of aerospace aluminium alloy 2024-T3. A solution-sonication mixing approach was applied to fabricate low-cost and high-efficiency layers. The coating system consisted of three main layers: pretreatment, green epoxy primer (GEP), and polyurethane (PU) top coating layer. To create the pretreatment layer, 50% graphene oxide (GOLMdis) was functionalized using a 50% 3-aminopropyltriethoxy silane coupling agent (SCA). The PU top coating layer was grafted with 1–3% wt. of GOLMdis to improve its resistance to environmental influences. Various analytical techniques such as Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Field emission scanning electron microscopy (FESEM), and Thermogravimetric analysis (TGA) were used to evaluate the properties of the PU-GO layer. The results confirmed the graft of GOLMdis onto the surface of PU, proving the existence of an interaction between the nanosheets and the polymer. The corrosion resistance performance of the multilayer coating systems was evaluated using electrochemical and adhesion tests. The 50%GO-SCA/Epoxy/2%GO-PU coating system outperforms other coating systems. It exhibited a significant improvement in the real impendence (Zre) by up to 9068 % compared to other coating systems. Moreover, only 5% of the coating was removed during the adhesion analysis. The results confirm the successful incorporation of anti-corrosion graphene oxide fillers into the multilayer coating system for corrosion protection of aerospace alloy 2024-T3.

Exploring relational aggression and gender dynamics: a global and Indonesian perspective

Girls generally use less physical aggression than boys. However, does this mean that girls are more likely to use relational aggression. With this question in mind, our study aims to analyze the relational aggression of students in Indonesia based on gender. This type of research used is comparative research. The sample in this study was 510 respondents consisting of 19.6% male and 80.4% female. The relational aggression scale (RAS) was used to collect data on student relational aggression in various regions in Indonesia. The analysis technique used is descriptive and differences with the Welch test. The results show that there are significant differences in male and female relational aggression, with an average score difference of -0.30 logit between men and women. In addition, there are fluctuations in the level of relational aggression in the context of gender from year to year in some countries, such as the United States, China, Australia, and others. Recommendations for future research are to integrate a more in-depth social and cultural analysis in Indonesia.

A soft computing algorithmic technique for circuital analysis of a wireless mobile charger

Wireless energy transfer is emerging as a promising technology for mobile devices because it enhances rapid charging without requiring conventional cables. In this paper, a wireless mobile charger circuit was designed and simulated, the data obtained thereof was used to train an artificial neural network (ANN) using Levenberg-Marquardt (LM) algorithm. The result obtained was validated against that obtained when trained with regular scaled conjugate algorithm. Analysis of the results showed that the proposed technique remains a viable technique for rapidly analyzing several parts of the wireless mobile charger circuit for design and educational purposes, without always executing computationally intensive and time-consuming simulations.

Synthesis of novel bioadhesive hydrogels via facile Thiol-Ene click chemistry for wound healing applications

Development of outstanding, cost-effective and elastic hydrogels as bioadhesive using Thiol-Ene click chemistry was verified. The visible light photocrosslinkable hydrogels composed of methacrylated chitosan/2,2′-(Ethylenedioxy) diethanethiol formed in presence of eosin-Y photoinitiator. Such hydrogels hold great promise for wound healing applications due to their tunable properties. Main components of hydrogels were extensively characterized using spectroscopic techniques for chemical analysis, thermal analysis, and topologic nanostructure. Various optimization conditions for best gelation time were investigated. Mechanical properties of tensile strength and elongation at break (%) were verified for best wound healing applications. Optimum hydrogel was subjected to for cytotoxicity and microbial suppression evaluation and in-vivo wound healing test for efficient wound healing evaluations. Our results demonstrate the potential use of injectable hydrogels as valuable bioadhesives in bioengineering and biomedical applications, particularly in wound closure and patches.

Economic optimization of hybrid renewable energy resources for rural electrification

In rural areas, grid expansions and diesel generators are commonly used to provide electricity, but their high maintenance costs and CO2 emissions make renewable energy sources (RES) a more practical alternative. Traditional methods such as analytical, statistical, and numerical-based techniques are inadequate for designing an energy-efficient RES. Therefore, this study utilized the bat algorithm (BA) to optimize the use of hybrid RES for rural electrification. A feasibility study was conducted in the village of Kalema to assess energy consumption, and a diesel-only system was modeled to serve the entire community. The BA was used to determine the optimal size and cost-effectiveness of the hybrid RES, with MATLAB R (2021a) utilized for simulation. The BA's performance was compared with diesel only and GA using cost of energy (COE) and CO2 emissions as metrics. Diesel generators only produced a COE of $6,562,000 and 1679.6 lb/hr of CO2 emissions. COE with BA was $356,9781.37 (a 45.6% reduction) and CO2 emissions were 635.29 lb/hr (a 62.2% drop). Genetic algorithm (GA) resulted in $364,3122.46 COE and 652.69 lb/hr CO2 emissions, indicating 61.1% and 44.5% decreases, respectively. BA significantly reduced COE and CO2 emissions over GA, according to the analysis.

Beyond the pandemic: understanding the perceived changes in mental health of the elderly population

This study aimed to describe the mental health of the elderly before and after the COVID-19 outbreak using a qualitative approach. Ten elderly people volunteered to participate in offline semi-structured interviews. The data was analyzed using the interpretative phenomenological analysis (IPA) technique. The results of our study mentioned the differences in the mental health of the elderly during the COVID-19 outbreak and after the COVID-19 outbreak. The elderly felt the difference in emotional terms, such as being more relieved and happier because activities returned to normal. The elderly feel calmer when using religion as religious coping to overcome psychological problems that arise during the COVID-19 outbreak. During the COVID-19 outbreak, elderly people still working also feel the impact on the family's economy, which compounds the problem. Excessive coverage on social media also helped make the elderly more afraid of COVID-19. The study's results can help improve older people's mental health after COVID-19 by understanding their experiences and finding good ways to cope. It suggests that future research should look at specific ways to support older people's mental health, taking into account their special challenges and the role of social media in causing fear and worry about COVID-19.

Effect of pulsed electric field processing on microbial and enzyme inactivation in blended plant-based milk alternatives: A case study on a microbial challenge test for a non-presterilized oat-based beverage enriched with pea protein

Microbial and enzyme inactivation by pulsed electric field (PEF) treatment was assessed in a blended plant-based milk alternative (PBMA) containing oats and pea protein without the application of presterilization. This was conducted to investigate a novel approach that permits PEF process validation for PBMA containing heat-labile components (protein and starch). Continuous PEF processing at moderate field strengths (9–10 kV/cm) combined with mild preheating (30–45 °C) for 25 s was applied over a range of specific energy inputs (77–245 kJ/L). Selective plating permitted bacterial enumeration of inoculated surrogate organism, despite the presence of natural microbial contaminants in the non-presterilized product. A minimum 5-log reduction of inoculated surrogates Escherichia coli and Listeria innocua alongside native microbiota was identified after preheating to 35–45 °C followed by PEF at 192–215 kJ/L. α-amylase activity was reduced by up to 89% during a similar PEF treatment (205 kJ/L, 40 °C). Overall, continuous PEF combined with mild preheating temperature was shown to achieve microbial inactivation in the pea protein enriched PBMA in compliance with current food safety regulations. Industrial relevancePresterilization is typically applied prior to conducting microbial challenge testing on food products to validate novel pasteurization treatments according to current regulatory requirements that a minimum 5-log reduction of relevant inoculated food pathogens must be achievable. However, for a variety of blended PBMA containing heat-labile proteins, presterilization is not advisable as the occurrence of irreversible changes in the product quality linked to protein coagulation and phase separation will affect the inactivation efficacy during subsequent microbial challenge testing. Hence, an alternative approach is presented utilizing a non-presterilized PBMA and thorough microbiological analysis, combining non-selective and selective plating techniques. This allows an identification of PEF treatment parameter with a minimum 5-log reduction of inoculated surrogate organism alongside naturally present microbial contaminants. This may serve as a valuable guideline for stakeholders in the food industry when addressing the efficacy of novel preservation treatments such as PEF in PBMA products with similar heat-induced product stability challenges.

High precision state of health estimation of lithium-ion batteries based on strong correlation aging feature extraction and improved hybrid kernel function least squares support vector regression machine model

The state of health (SOH) of lithium-ion batteries plays a crucial role in maintaining the stability of electric vehicle systems. To address the issue of low accuracy in existing prediction models, this article introduces an enhanced grey wolf algorithm for optimizing the hybrid kernel least squares support vector regression machine used in lithium-ion battery SOH prediction. This research extracted four key health features from the raw data of each battery in the Cycle dataset, which is publicly accessible. Data preprocessing of health features involved Pearson correlation analysis and Hampel filtering techniques. The framework of least squares support vector regression constructs a hybrid kernel function of polynomial kernel function and radial basis function. The integration of differential evolution and the law of survival of the fittest into the grey wolf algorithm enhances its optimization ability. The improved grey wolf algorithm optimizes the parameters of the hybrid kernel least squares support vector regression machine, improving the accuracy and robustness of the model. After data validation, it is known that the optimal average absolute error value predicted by the model can reach 0.32 %. This indicates that the proposed method is effective and feasible.

Exploratory Factor Analysis on Acceptance of Artificial Intelligence as Online Healthcare Product Sales Advisor Among Consumers of Guardian Pharmaceutical Outlets in Malaysia

Switching from human to artificial intelligence (AI) as online health sales advisors significantly impact sales. AI has been shown to influence customer purchases and adapt quickly to changes in online retail. While AI enhances efficiency and personalisation in sales, it also introduces ethical dilemmas, such as job loss and bias, alongside consumer scepticism about its accuracy and trustworthiness. Integrating AI into pharmacy practices demands a deep understanding of its effects on pharmacists’ roles, signaling a significant shift in healthcare delivery. This study aims to explore and confirm the seven Unified Theory of Acceptance and Use of Technology 1 (UTAUT1) construct as a robust framework that can be utilized in the consumer acceptance of AI technologies in healthcare retail settings. The participants in the study were patrons of the Guardian Pharmacy in the Klang Valley region. The research team modified 46 questions from an earlier study, employing a dual translation method that involved translating the questionnaire from English to Malay and then back to English to better align with the demographics of the study group. The experts assessed and authenticated these revised statements to ensure their relevance and accuracy, focusing on content and appearance authenticity. The study analyzed and confirmed 103 responses using the exploratory factor analysis (EFA) technique. The EFA results indicated that each construct’s items were grouped into one component with factor loading higher than 0.6, with the total variance explained ranging from 69% to 90% and overall internal reliability of 0.944. This study enriches existing knowledge by validating the UTAUT1 framework as a robust framework in assessing consumer acceptance towards AI integration in healthcare sales, marking a significant shift in healthcare delivery and underscoring the need for a nuanced understanding of AI’s implications on pharmacists’ roles amidst ethical and consumer.

Nanostructured Iron (III)-Copper (II) Binary Oxide as a Highly Efficient Magnetically Recoverable Nanocatalyst for Facile One-pot Synthesis of 2, 4, 5-trisubstituted Imidazole and 1, 4-dihydro Pyridine Derivatives under Solvent-free Conditions

Abstract: The Fe (III)-Cu (II) binary oxide magnetic nanocatalyst emerges as an environmentally friendly and highly efficient solid acid catalyst, demonstrating remarkable utility in the one-pot synthesis of 2, 4, 5-trisubstituted imidazole and 1,4-dihydropyridine compounds, all achieved under solvent-free conditions. A facile co-precipitation method was used to synthesize nanostructured Fe-Cu binary oxide. Notably, this Fe-Cu binary oxide magnetic nanocatalyst proves its eco-friendly credentials as an exceptionally efficient and reusable catalyst, offering ease of handling, recovery, and multiple uses with minimal reactivity loss. Furthermore, the Fe (III)-Cu (II) binary oxide magnetic nanocatalyst's magnetic separability enhances its practicality, allowing for effortless catalyst retrieval after reactions. Significantly, the structural characteristics are meticulously elucidated through advanced analytical techniques, including 1H and 13C nuclear magnetic resonance (NMR) spectroscopy. This work presents a versatile and sustainable solution for catalysis, with wide-reaching implications for green chemistry and the development of reusable, efficient catalysts for organic synthesis. The exceptional performance and eco-friendliness of the Fe-Cu binary oxide magnetic nanocatalyst underscore its practical significance. Fe-Cu binary oxide magnetic nanocatalyst exhibits the highest catalytic activity compared to others. The employment of this catalyst consistently delivers excellent yields in the target reactions, highlighting its potential to contribute positively to sustainable chemical processes.

A review on features and methods of potential fishing zone

This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.

Isolation and characterization of secondary metabolites from Bryophylum pinnatum (Lam.) Oken and assessment of wound healing efficacy using animal model

Wound healing is a complex and intrinsic process that implicates various stages in skin tissue regeneration, may further improvise using secondary metabolites. During a screening of plants traditionally utilized for wound healing, especially by tribals, we encountered Bryophyllum pinnatum (Lam.) Oken Plant. Further, we considered the leaf extract of Bryophyllum pinnatum (Lam.) Oken in various solvents for In vivo characterization of wound healing. The leaf extracts were prepared in petroleum ether, ethyl acetate, and ethanol and further identified the phytochemicals via advanced analytical techniques. The solvent extracts in the ointment base (1 % and 2 % w/w) were prepared and tested against the acute and burn wound models along with control and positive control (Cipladine 1 %). The ethanolic extract had higher phytochemical constituents was tested against the rat model, was found that 2 % w/w ethanolic ointment base stimulated the wound healing area, hydroxyproline content, and wound index and simultaneously reduced the epithelization period compared to the control. The present study concluded that the Ethanolic extract of Bryophyllum pinnatum leaves loaded ointment further promote the % of wound contraction rate, epithelization, and promotion of angiogenesis, exhibited the potential to be utilized to treat wounds.

Biological Potential of a Bibenzyl Compound ‘Gigantol’ for the Treatment of Human Disorders: Pharmacological Activities and Analytical Aspects of an Active Phytochemical Isolated from Orchid

Background: Dendrobium chrysotoxum Lindl. is an important medicinal plant of the genus Dendrobium from the Orchidaceae family. Gigantol is one of the key bioactive phytochemicals found in Dendrobium plants. Gigantol is reported to have diverse pharmacological activities Aims: This narrative review explores the analytical aspects along with pharmacological activities of gigantol as reported in different scientific publications. Methods: To find appropriate information related to Dendrobium plants and gigantol, extensive data extraction was done using ScienceDirect, Google, PubMed, and Scopus databases, and diverse facts were collected, arranged and analyzed to know the therapeutic potential of gigantol. Analytical aspects of gigantol were also discussed in the present work. Results: Gigantol has a wide distribution in the Dendrobium officinale, Dendrobium chrysanthum, Dendrobium crystallinum, Dendrobium aphyllum, and Dendrobium devonianum. Available data indicates diverse pharmacological activities of gigantol. Preclinical studies have shown its effectiveness in the treatment of cataractogenesis, liver injury, leishmaniasis, nephrotoxicity, spasm, and skin disorders. Gigantol has been found to control hepatocellular cancer, lung cancer, breast cancer, bladder cancer, and cervical cancer. The neuroprotective, antinociceptive, anti-inflammatory, antioxidant, vasorelaxant, immune modulatory effect, antimalarial, and anti-herpetic properties of gigantol have also been observed. Applications of different analytical techniques for the isolation and characterization of gigantol were also discussed in detail. Conclusion: Gigantol has significant and diverse pharmacological activities that must be explored in clinical setup to develop therapeutic leads for different diseases and health conditions.

Non-contact Testing of Large Concrete Structures for Voids and Delaminations

Concrete is the most used construction material for civil engineering structures, as it is reliable, cost-efficient, and long lasting. Infrastructure made of reinforced concrete can be used for many decades, provided that maintenance is rigorous. The necessary periodic checks are typically manual tasks, including visual inspection for defects on the outside and hammering to find internal voids and imminent spalling. In this a paper, a non-contact system for automated inspection is presented. Two parallel lasers are used with a scanning head on a mobile platform to cover large areas. The first laser emits a high energy pulse, igniting plasma on the surface and subsequently inducing a shock wave into the structure. The second laser measures the resulting vibration. Signal analysis of individual measurement points give a first indication about the presence of faults. Unsupervised learning and visual computing are used to find and narrow down damaged areas in the scanning data with higher probability. After systematic testing in the laboratory, measurements were conducted recently in two tunnels with concrete linings. This paper provides an overview of the developed hardware and signal analysis techniques. Furthermore, the recent verification results are presented for the first time. In future, the proposed method can be combined with geometric scans and automated optical inspection of the surface to offer a holistic inspection approach and relieve the authorities that are responsible for operation and maintenance of the growing number of aging infrastructures.